Anticipated Impacts on Veterans Health Care: VA set an ambitious goal for ending homelessness among Veterans by 2015. HUD-VASH and Grant & Per Diem (GPD) are two primary VA housing programs to help Veterans exit homelessness. Substance abuse among Veterans in VA housing is a major risk factor for worsening psychopathology and housing instability. Despite recent adoption of a Housing First approach, many VA housing programs lack the staff infrastructure to manage substance use disorders (SUDs). This is a significant problem as 60% (conservative estimate) of Veterans in VA housing have SUDs and these Veterans show greater psychopathology compared to their counterparts without SUDs. These issues warrant substantial need for SUD programs in VA housing that are efficient to deploy, easily layered onto existing services, and require minimal staff to operate. The current study addresses this current void in VA treatment services in that it investigates the effectiveness, implementation process, and cost estimate of treatment of an adaptation of motivational interviewing, an empirically supported intervention with strong impact on reducing substance use and enhancing treatment engagement, in a group format, referred to as Group Motivational Interviewing (GMI) for Veterans with SUDs in VA housing. Data from this project, if shown to be promising, will establish the basis of a GMI dissemination and implementation course of action for highly vulnerable homeless Veterans in VA housing for achieving their greatest success in attaining housing stability. Background: There is a significant need for 'wraparound' treatment services in VA housing for addressing SUDs. Homeless Veterans with SUDs are vulnerable to treatment dropout, rendering them susceptible to relapse, while their continuation in outpatient care during their participation in VA housing leads to improved clinical outcomes. According to systematic reviews, individual MI reduces the incidence of SUD, when compared to no treatment, but is labor intensive. As VA moves toward a 'Housing First' paradigm where greater numbers of homeless Veterans will continue to use substances while in VA housing, delivery of GMI (which may be less labor intensive) to these patients will be important for initiating and maintaining their recovery as well as enhancing their psychosocial integration and quality of life. In a prior controlled trial conducted by the PI, GMI resulted in significantly higher outpatient treatment engagement and lower substance use compared to treatment-as-usual among dually diagnosed Veterans. Objectives: Study objectives are consistent with VA housing recommendations focusing on patient recovery, health services promotion, and treatment implementation evaluation. GMI will be compared to a control treatment condition (CT) on (Specific Aim I; Five outcomes: (Primary H1): treatment engagement; (Primary H1): substance use; (Secondary H2): psychosocial integration (e.g., social support, community participation); (Secondary H3) quality of life/psychiatric indices; and (Secondary H4): number of days engaging in structured/productive work activities in the 6-month follow up. Specific Aim II involves a process evaluation for documenting (A) formative (e.g., developmental), (B) process, and (C) summative outcomes; and Specific Aim III involves estimation of cost of intervention in terms of direct costs, indirect costs of staff, costs of capital and workload measures for future implementation and dissemination research. Methods: Randomized controlled trial comparing GMI to CT across five critical outcomes. 186 Veterans in VA housing services (93 per treatment arm) will be enrolled with a diagnosis of alcohol or drug abuse/dependence. Recruitment will take place in Charleston VAMC HUD-VASH & GPD. Participants will be randomly assigned to (1) GMI or (2) CT, each consisting of 4 sessions, and will be evaluated at 1, 3, and 6 months. Participants with a non-substance related DSM-IV-TR major Axis I disorder (e.g., MDD, PTSD) will be eligible for the study. Analyses will be conducted using generalized linear mixed models (GLMM) approach.